Improving Audit Effectiveness / Efficiency by Leveraging Data Analytics
- 1. Improving Audit Effectiveness / Efficiency by Leveraging Data Analytics 12 May 2016
- 2. Arrow Audit DA Journey Pre-2015 •1 staff •10 analytics – AP, T&E 2015 •Invested in enhanced skillsets & technology •260+ analytics •Data visualization •Self service •Financial Close Toolkit •Manual JEs analytics 2016 •Dedicate more finance and accounting resources •Statistical Modeling •Behavioral and predictive analytics
- 3. Analytics Defined Data presentation Statistical models Subject matter knowledge Technical expertise Discovery & communication of meaningful patterns Audit Team
- 4. Common Challenges According to a KPMG study, Audit departments are challenged by: • Disparate systems supporting different business models (e.g. T&E) • Establishing the definition of an “exception”, addressing “false positives” and “false negatives” • Bridging the gap on what the audit population is (e.g. Benford’s) • Relying on intuition rather than data to support audit risk assessment (e.g. defining a manual JE) “Data analytics will likely be unsustainable without linkage to, or integration with, an audit work plan and the related audit objectives.”
- 5. Our Challenges 1.Data acquisition – understanding and processing the data; need to start with client-provided data as a base and then become more independent as you get comfortable with the data 2.Finding the right resources – BI, Auditor, Business Analyst? 3.Bandwidth 4.Technology needs 5.Over-dependence by auditors – analytics are just the beginning of the audit dialog
- 6. What we can do • Understanding process is critical to provide valuable analysis • Right sizing the analytics for the size of the organization and risks being assessed • Continuous improvement on analytics effectiveness
- 7. Audit Data Analytics Lifecycle Planning Brainstorming Session Communicate scope & objectives Understand business context Fieldwork Knowledge sharing Integrate DA documentation Reporting Integrate analytics Feedback on use of analytics
- 8. Program Management Establish development methodology (e.g. Agile) Business process driven Audit Data Analytics Key Elements Access Data Acquisition Tools Understand business processes Identify data sources Establish data acquisition approach (direct connection, backup restoration, system canned reports, etc. Evaluation of development tools Excel SQL ACL R/Python Tableau / QlikSense Understand what data is captured by the source system Examine the data quality, integrity, and completeness Design testing approach based on the data obtained Data Source Project Management
- 9. Data Analytics to Start With Accounting Analytics
- 10. When Benford Analysis Is or Is Not Likely Useful When Benford Analysis is Likely Useful Examples Sets of numbers that result form mathematical combination of numbers AR (number sold *price), AP (number bought * price) Transaction-level date – no need to sample Disbursement, sales, expenses On large data sets – The more observations, the better Full year’s transactions When Benford Analysis is Not Likely Useful Examples Data set is comprised of assigned numbers Check numbers, invoice numbers, zip codes Numbers that are influenced by human thoughts Prices set at psychological thresholds($1.99), ATM withdraws Accounts with a build in minimum or maximum Set of assets that must meet a threshold to be recorded
- 11. Key Analytics The Wharton School has published basic data analytical tests that can assist in re- focusing efforts in planning and executing audits in areas that could indicate incentives for management to manipulate results. • These tests fall into the following areas: – Dupont Analysis – Revenue & Expense Recognition Management – Discretionary Accruals & Expenditures – Fraud Prediction – Beneish M-Score
- 12. DuPont Analysis
- 14. Revenue Recognition Red Flags Potential red flags that identify potential changes in revenue recognition policies: • Unusual seasonally-adjusted quarterly (monthly) trends • Growth in Revenue • Growth in Accounts Receivable • Unusual trends in Ratios • Days Receivable and Accounts Receivable/Revenue Then, we will try to find what happened • Do earnings management incentives exist? • Is there anything unusual in the Revenue Recognition policy
- 15. Year-over-Year Growth Trends Due to seasonality need to compare to same quarter / month of the prior year • YoY Revenue Growth • YoY Growth in AR Benchmarks • Time-series: is growth unusual in one specific quarter for the firm? • Cross-sectional: is growth unusual for the industry in a given quarter?
- 18. Fraud Prediction • Fraud prediction models examine companies that have been caught committing fraud to model how they differ from companies not caught • Uses statistical techniques to chose a small set of ratios Advantages – Specifically tailored to characteristics of fraud firms – Model parameters are fixed and don’t have to be re-estimated for each company Disadvantages – Models based on companies that were caught with large frauds M-Score is based on eight ratios – Higher M-Score means higher likelihood of manipulation – Uses comparisons between current year and prior year
Editor's Notes
- #4: Statistics Using standard deviation to ID unusual Jes Benfords Subject matter knowledge What is the data telling us in the context of the business process Data Presentation 1. Need to understand how the data is going to be used whether it is Excel, Tableau, or something else.